Prediction of genetic connectivity in urban ecosystems by combining detailed movement data, genetic data and multi-path modelling

Abstract

Urban areas are expanding worldwide, yet little is known how anthropogenic landscape fragmentation affects the connectedness and gene flow in urban wildlife. The European hedgehog (Erinaceus europaeus) is a ground dwelling mammal which also inhabits variable urban habitats. We investigated habitat connectivity and the spatial genetic structure of urban hedgehogs in the largest Swiss city. We addressed the following questions: i) At the city-scale, which prominent landscape elements affect the spatial distribution of genetic clusters? ii) Which landscape elements affect gene flow in an urban mammal within the clusters? iii) Does individual movement data improve the prediction of landscape-wide gene flow?We used two Bayesian methods to examine the influence of water bodies and major traffic routes on genetic hedgehog clusters, using microsatellite data of 147 hedgehogs. Further, we used extensive movement data to parameterise single-path and multi-path connectivity models, which were then used to predict genetic distance between hedgehog individuals.First, we found that both Bayesian methods consistently showed three distinct genetic clusters, separated by the main rivers and the parallel running transportation axes. Second, the best model indicates that gene flow was facilitated by urban green areas and hampered by all other land cover types. Third, multi-path models based on detailed GPS movement data clearly outperformed models based on a priori assumptions to predict gene flow.Multi-path connectivity models based on movement data reveal to be a powerful tool to detect gene flow in highly fragmented habitats and could be a crucial step in implementing effective conservation measures.